serving_embedding.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import base64
from collections.abc import AsyncGenerator, Mapping
from typing import Any, Final, Literal, Optional, Union, cast

import numpy as np
import torch
from fastapi import Request
from typing_extensions import assert_never, override

from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.protocol import (
    EmbeddingChatRequest,
    EmbeddingCompletionRequest,
    EmbeddingRequest,
    EmbeddingResponse,
    EmbeddingResponseData,
    ErrorResponse,
    UsageInfo,
)
from vllm.entrypoints.openai.serving_engine import (
    EmbeddingServeContext,
    OpenAIServing,
    ServeContext,
    TextTokensPrompt,
)
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
from vllm.entrypoints.renderer import RenderConfig
from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
from vllm.logger import init_logger
from vllm.outputs import (
    EmbeddingOutput,
    EmbeddingRequestOutput,
    PoolingOutput,
    PoolingRequestOutput,
    RequestOutput,
)
from vllm.pooling_params import PoolingParams
from vllm.utils import chunk_list

logger = init_logger(__name__)


def _get_embedding(
    output: EmbeddingOutput,
    encoding_format: Literal["float", "base64"],
) -> Union[list[float], str]:
    if encoding_format == "float":
        return output.embedding
    elif encoding_format == "base64":
        # Force to use float32 for base64 encoding
        # to match the OpenAI python client behavior
        embedding_bytes = np.array(output.embedding, dtype="float32").tobytes()
        return base64.b64encode(embedding_bytes).decode("utf-8")

    assert_never(encoding_format)


class EmbeddingMixin(OpenAIServing):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        pooler_config = self.model_config.pooler_config

        # Avoid repeated attribute lookups
        self.supports_chunked_processing = bool(
            pooler_config and pooler_config.enable_chunked_processing
        )
        self.max_embed_len = (
            pooler_config.max_embed_len
            if pooler_config and pooler_config.max_embed_len
            else None
        )

    @override
    async def _preprocess(
        self,
        ctx: ServeContext,
    ) -> Optional[ErrorResponse]:
        ctx = cast(EmbeddingServeContext, ctx)
        try:
            ctx.lora_request = self._maybe_get_adapters(ctx.request)

            tokenizer = await self.engine_client.get_tokenizer()
            renderer = self._get_renderer(tokenizer)

            if isinstance(ctx.request, EmbeddingChatRequest):
                (
                    _,
                    _,
                    ctx.engine_prompts,
                ) = await self._preprocess_chat(
                    ctx.request,
                    tokenizer,
                    ctx.request.messages,
                    chat_template=ctx.request.chat_template or ctx.chat_template,
                    chat_template_content_format=ctx.chat_template_content_format,
                    add_generation_prompt=ctx.request.add_generation_prompt,
                    continue_final_message=False,
                    add_special_tokens=ctx.request.add_special_tokens,
                )
            else:
                ctx.engine_prompts = await renderer.render_prompt(
                    prompt_or_prompts=ctx.request.input,
                    config=self._build_render_config(ctx.request),
                )
            return None
        except (ValueError, TypeError) as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))

    def _build_render_config(self, request: EmbeddingCompletionRequest) -> RenderConfig:
        # Set max_length based on chunked processing capability
        if self._should_use_chunked_processing(request):
            max_length = None
        else:
            max_length = self.max_embed_len or self.max_model_len

        return RenderConfig(
            max_length=max_length,
            truncate_prompt_tokens=request.truncate_prompt_tokens,
            add_special_tokens=request.add_special_tokens,
        )

    @override
    def _build_response(
        self,
        ctx: ServeContext,
    ) -> Union[EmbeddingResponse, ErrorResponse]:
        items: list[EmbeddingResponseData] = []
        num_prompt_tokens = 0

        final_res_batch_checked = cast(list[PoolingRequestOutput], ctx.final_res_batch)

        for idx, final_res in enumerate(final_res_batch_checked):
            embedding_res = EmbeddingRequestOutput.from_base(final_res)

            item = EmbeddingResponseData(
                index=idx,
                embedding=_get_embedding(
                    embedding_res.outputs, ctx.request.encoding_format
                ),
            )
            prompt_token_ids = final_res.prompt_token_ids

            items.append(item)
            num_prompt_tokens += len(prompt_token_ids)

        usage = UsageInfo(
            prompt_tokens=num_prompt_tokens,
            total_tokens=num_prompt_tokens,
        )

        return EmbeddingResponse(
            id=ctx.request_id,
            created=ctx.created_time,
            model=ctx.model_name,
            data=items,
            usage=usage,
        )

    def _get_max_position_embeddings(self) -> int:
        """Get the model's effective maximum sequence length for chunking."""
        return self.model_config.max_model_len

    def _should_use_chunked_processing(self, request) -> bool:
        """Check if chunked processing should be used for this request."""
        return (
            isinstance(request, (EmbeddingCompletionRequest, EmbeddingChatRequest))
            and self.supports_chunked_processing
        )

    async def _process_chunked_request(
        self,
        ctx: EmbeddingServeContext,
        original_prompt: TextTokensPrompt,
        pooling_params,
        trace_headers,
        prompt_idx: int,
    ) -> list[AsyncGenerator[PoolingRequestOutput, None]]:
        """Process a single prompt using chunked processing."""
        generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
        token_ids = original_prompt["prompt_token_ids"]

        # Split into chunks using max_position_embeddings
        max_pos_embeddings = self._get_max_position_embeddings()
        # Process all chunks for MEAN aggregation
        for chunk_idx, chunk_tokens in enumerate(
            chunk_list(token_ids, max_pos_embeddings)
        ):
            # Create a request ID for this chunk
            chunk_request_id = f"{ctx.request_id}-prompt-{prompt_idx}-chunk-{chunk_idx}"

            # Create engine prompt for this chunk
            chunk_engine_prompt = EngineTokensPrompt(prompt_token_ids=chunk_tokens)

            # Create chunk request prompt for logging
            chunk_text = ""
            chunk_request_prompt = TextTokensPrompt(
                prompt=chunk_text, prompt_token_ids=chunk_tokens
            )

            # Log the chunk
            self._log_inputs(
                chunk_request_id,
                chunk_request_prompt,
                params=pooling_params,
                lora_request=ctx.lora_request,
            )

            # Create generator for this chunk and wrap it to return indices
            original_generator = self.engine_client.encode(
                chunk_engine_prompt,
                pooling_params,
                chunk_request_id,
                lora_request=ctx.lora_request,
                trace_headers=trace_headers,
                priority=getattr(ctx.request, "priority", 0),
            )

            generators.append(original_generator)

        return generators

    def _validate_input(
        self,
        request,
        input_ids: list[int],
        input_text: str,
    ) -> TextTokensPrompt:
        """Override to support chunked processing for embedding requests."""
        token_num = len(input_ids)

        # Note: EmbeddingRequest doesn't have max_tokens
        if isinstance(request, (EmbeddingCompletionRequest, EmbeddingChatRequest)):
            # Check if chunked processing is enabled for pooling models
            enable_chunked = self._should_use_chunked_processing(request)

            # Use max_position_embeddings for chunked processing decisions
            max_pos_embeddings = self._get_max_position_embeddings()

            # Determine the effective max length for validation
            if self.max_embed_len is not None:
                # Use max_embed_len for validation instead of max_model_len
                length_type = "maximum embedding input length"
                max_length_value = self.max_embed_len
            else:
                # Fall back to max_model_len validation (original behavior)
                length_type = "maximum context length"
                max_length_value = self.max_model_len

            validation_error_msg = (
                "This model's {length_type} is {max_length_value} tokens. "
                "However, you requested {token_num} tokens in the input for "
                "embedding generation. Please reduce the length of the input."
            )

            chunked_processing_error_msg = (
                "This model's {length_type} is {max_length_value} tokens. "
                "However, you requested {token_num} tokens in the input for "
                "embedding generation. Please reduce the length of the input "
                "or enable chunked processing."
            )

            # Check if input exceeds max length
            if token_num > max_length_value:
                raise ValueError(
                    validation_error_msg.format(
                        length_type=length_type,
                        max_length_value=max_length_value,
                        token_num=token_num,
                    )
                )

            # Check for chunked processing
            # when exceeding max_position_embeddings
            if token_num > max_pos_embeddings:
                if enable_chunked:
                    # Allow long inputs when chunked processing is enabled
                    logger.info(
                        "Input length %s exceeds max_position_embeddings "
                        "%s, will use chunked processing",
                        token_num,
                        max_pos_embeddings,
                    )
                else:
                    raise ValueError(
                        chunked_processing_error_msg.format(
                            length_type="maximum position embeddings length",
                            max_length_value=max_pos_embeddings,
                            token_num=token_num,
                        )
                    )

            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

        # For other request types, use the parent's implementation
        return super()._validate_input(request, input_ids, input_text)

    def _is_text_tokens_prompt(self, prompt) -> bool:
        """Check if a prompt is a TextTokensPrompt (has prompt_token_ids)."""
        return (
            isinstance(prompt, dict)
            and "prompt_token_ids" in prompt
            and "prompt_embeds" not in prompt
        )

    async def _create_single_prompt_generator(
        self,
        ctx: EmbeddingServeContext,
        engine_prompt: EngineTokensPrompt,
        pooling_params: PoolingParams,
        trace_headers: Optional[Mapping[str, str]],
        prompt_index: int,
    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
        """Create a generator for a single prompt using standard processing."""
        request_id_item = f"{ctx.request_id}-{prompt_index}"

        self._log_inputs(
            request_id_item,
            engine_prompt,
            params=pooling_params,
            lora_request=ctx.lora_request,
        )

        # Return the original generator without wrapping
        return self.engine_client.encode(
            engine_prompt,
            pooling_params,
            request_id_item,
            lora_request=ctx.lora_request,
            trace_headers=trace_headers,
            priority=getattr(ctx.request, "priority", 0),
        )

    @override
    async def _prepare_generators(
        self,
        ctx: ServeContext,
    ) -> Optional[ErrorResponse]:
        """Override to support chunked processing."""
        ctx = cast(EmbeddingServeContext, ctx)

        # Check if we should use chunked processing
        use_chunked = self._should_use_chunked_processing(ctx.request)

        # If no chunked processing needed, delegate to parent class
        if not use_chunked:
            return await super()._prepare_generators(ctx)

        # Custom logic for chunked processing
        generators: list[
            AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]
        ] = []

        try:
            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )

            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params

            # Verify and set the task for pooling params
            try:
                pooling_params.verify("embed", self.model_config)
            except ValueError as e:
                return self.create_error_response(str(e))

            if ctx.engine_prompts is None:
                return self.create_error_response("Engine prompts not available")

            max_pos_embeddings = self._get_max_position_embeddings()

            for i, engine_prompt in enumerate(ctx.engine_prompts):
                # Check if this specific prompt needs chunked processing
                if self._is_text_tokens_prompt(engine_prompt):
                    # Cast to TextTokensPrompt since we've verified
                    # prompt_token_ids
                    text_tokens_prompt = cast(TextTokensPrompt, engine_prompt)
                    if len(text_tokens_prompt["prompt_token_ids"]) > max_pos_embeddings:
                        # Use chunked processing for this prompt
                        chunk_generators = await self._process_chunked_request(
                            ctx, text_tokens_prompt, pooling_params, trace_headers, i
                        )
                        generators.extend(chunk_generators)
                        continue

                # Normal processing for short prompts or non-token prompts
                generator = await self._create_single_prompt_generator(
                    ctx, engine_prompt, pooling_params, trace_headers, i
                )
                generators.append(generator)

            from vllm.utils import merge_async_iterators

            ctx.result_generator = merge_async_iterators(*generators)

            return None

        except Exception as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

    @override
    async def _collect_batch(
        self,
        ctx: ServeContext,
    ) -> Optional[ErrorResponse]:
        """Collect and aggregate batch results
        with support for chunked processing.

        For chunked requests, performs online aggregation to
        minimize memory usage.
        For regular requests, collects results normally.
        """
        ctx = cast(EmbeddingServeContext, ctx)
        try:
            if ctx.engine_prompts is None:
                return self.create_error_response("Engine prompts not available")

            # Check if we used chunked processing
            use_chunked = self._should_use_chunked_processing(ctx.request)

            if not use_chunked:
                return await super()._collect_batch(ctx=ctx)

            if ctx.result_generator is None:
                return self.create_error_response("Result generator not available")

            # Online aggregation for chunked requests to
            # minimize memory usage
            # Track aggregation state for each prompt
            prompt_aggregators: dict[int, dict[str, Any]] = {}
            short_prompts_results: dict[int, PoolingRequestOutput] = {}

            async for result_idx, result in ctx.result_generator:
                if "-chunk-" in result.request_id:
                    # Extract prompt_idx from chunked request_id
                    parts = result.request_id.split("-")
                    try:
                        prompt_idx = int(parts[parts.index("prompt") + 1])
                    except (ValueError, IndexError):
                        # Fallback: extract from result_idx if parsing fails
                        prompt_idx = result_idx

                    # Initialize aggregator for this prompt if needed
                    if prompt_idx not in prompt_aggregators:
                        prompt_aggregators[prompt_idx] = {
                            "weighted_sum": None,
                            "total_weight": 0,
                            "chunk_count": 0,
                            "request_id": result.request_id.split("-chunk-")[0],
                        }

                    aggregator = prompt_aggregators[prompt_idx]

                    # MEAN pooling with online weighted averaging
                    # Ensure result is PoolingRequestOutput
                    # for embedding processing
                    if not isinstance(result, PoolingRequestOutput):
                        return self.create_error_response(
                            f"Expected PoolingRequestOutput for "
                            f"chunked embedding, got "
                            f"{type(result).__name__}"
                        )

                    # Handle both PoolingOutput and
                    # EmbeddingOutput types
                    if hasattr(result.outputs, "data"):
                        # PoolingOutput case
                        embedding_data = result.outputs.data
                    elif hasattr(result.outputs, "embedding"):
                        # EmbeddingOutput case -
                        # convert embedding list to tensor
                        embedding_data = result.outputs.embedding
                    else:
                        return self.create_error_response(
                            f"Unsupported output type: {type(result.outputs).__name__}"
                        )

                    if not isinstance(embedding_data, torch.Tensor):
                        embedding_data = torch.tensor(
                            embedding_data, dtype=torch.float32
                        )

                    if result.prompt_token_ids is None:
                        return self.create_error_response(
                            "prompt_token_ids cannot be None for chunked processing"
                        )
                    weight = len(result.prompt_token_ids)

                    weighted_embedding = embedding_data.to(dtype=torch.float32) * weight

                    if aggregator["weighted_sum"] is None:
                        # First chunk
                        aggregator["weighted_sum"] = weighted_embedding
                    else:
                        # Accumulate
                        aggregator["weighted_sum"] += weighted_embedding

                    aggregator["total_weight"] += weight
                    aggregator["chunk_count"] += 1
                else:
                    # Non-chunked result - extract prompt_idx from request_id
                    parts = result.request_id.split("-")
                    try:
                        # Last part should be prompt index
                        prompt_idx = int(parts[-1])
                    except (ValueError, IndexError):
                        prompt_idx = result_idx  # Fallback to result_idx

                    short_prompts_results[prompt_idx] = cast(
                        PoolingRequestOutput, result
                    )

            # Finalize aggregated results
            final_res_batch: list[
                Union[PoolingRequestOutput, EmbeddingRequestOutput]
            ] = []
            num_prompts = len(ctx.engine_prompts)

            for prompt_idx in range(num_prompts):
                if prompt_idx in prompt_aggregators:
                    # Finalize MEAN aggregation for this chunked prompt
                    aggregator = prompt_aggregators[prompt_idx]

                    weighted_sum = aggregator["weighted_sum"]
                    total_weight = aggregator["total_weight"]

                    if (
                        weighted_sum is not None
                        and isinstance(weighted_sum, torch.Tensor)
                        and isinstance(total_weight, (int, float))
                        and total_weight > 0
                    ):
                        # Compute final mean embedding
                        final_embedding = weighted_sum / total_weight

                        # Create a PoolingRequestOutput
                        # for the aggregated result
                        pooling_output_data = PoolingOutput(data=final_embedding)

                        # Get original prompt token IDs for this prompt
                        original_prompt = ctx.engine_prompts[prompt_idx]
                        if not self._is_text_tokens_prompt(original_prompt):
                            return self.create_error_response(
                                f"Chunked prompt {prompt_idx} is not a TextTokensPrompt"
                            )

                        original_token_ids = cast(TextTokensPrompt, original_prompt)[
                            "prompt_token_ids"
                        ]

                        pooling_request_output = PoolingRequestOutput(
                            request_id=aggregator["request_id"],
                            prompt_token_ids=original_token_ids,
                            outputs=pooling_output_data,
                            finished=True,
                        )

                        final_res_batch.append(pooling_request_output)
                    else:
                        return self.create_error_response(
                            f"Failed to aggregate chunks for prompt {prompt_idx}"
                        )
                elif prompt_idx in short_prompts_results:
                    final_res_batch.append(
                        cast(PoolingRequestOutput, short_prompts_results[prompt_idx])
                    )
                else:
                    return self.create_error_response(
                        f"Result not found for prompt {prompt_idx}"
                    )

            ctx.final_res_batch = cast(
                list[Union[RequestOutput, PoolingRequestOutput]], final_res_batch
            )

            return None

        except Exception as e:
            return self.create_error_response(str(e))


class OpenAIServingEmbedding(EmbeddingMixin):
    request_id_prefix = "embd"

    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        *,
        request_logger: Optional[RequestLogger],
        chat_template: Optional[str],
        chat_template_content_format: ChatTemplateContentFormatOption,
        trust_request_chat_template: bool = False,
        log_error_stack: bool = False,
    ) -> None:
        super().__init__(
            engine_client=engine_client,
            models=models,
            request_logger=request_logger,
            log_error_stack=log_error_stack,
        )

        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
        self.trust_request_chat_template = trust_request_chat_template

    async def create_embedding(
        self,
        request: EmbeddingRequest,
        raw_request: Optional[Request] = None,
    ) -> Union[EmbeddingResponse, ErrorResponse]:
        """
        Embedding API similar to OpenAI's API.

        See https://platform.openai.com/docs/api-reference/embeddings/create
        for the API specification. This API mimics the OpenAI Embedding API.
        """
        model_name = self.models.model_name()
        request_id = (
            f"{self.request_id_prefix}-"
            f"{self._base_request_id(raw_request, request.request_id)}"
        )

        ctx = EmbeddingServeContext(
            request=request,
            raw_request=raw_request,
            model_name=model_name,
            request_id=request_id,
            chat_template=self.chat_template,
            chat_template_content_format=self.chat_template_content_format,
        )

        return await super().handle(ctx)  # type: ignore

    @override
    def _create_pooling_params(
        self,
        ctx: ServeContext[EmbeddingRequest],
    ) -> Union[PoolingParams, ErrorResponse]:
        pooling_params = super()._create_pooling_params(ctx)
        if isinstance(pooling_params, ErrorResponse):
            return pooling_params

        try:
            pooling_params.verify("embed", self.model_config)
        except ValueError as e:
            return self.create_error_response(str(e))

        return pooling_params

    async def _preprocess(
        self,
        ctx: ServeContext,
    ) -> Optional[ErrorResponse]:
        if isinstance(ctx.request, EmbeddingChatRequest):
            error_check_ret = self._validate_chat_template(
                request_chat_template=ctx.request.chat_template,
                chat_template_kwargs=ctx.request.chat_template_kwargs,
                trust_request_chat_template=self.trust_request_chat_template,
            )
            if error_check_ret is not None:
                return error_check_ret
        return await super()._preprocess(ctx)